1. Field of the Invention
The present invention relates generally to a data processing system. More specifically, the present invention relates to a computer implemented method, apparatus, and computer program product for building data used by the understanding model of a natural language call routing application.
2. Description of the Related Art
Currently there are two predominant models for automated call center routing: (i) a directed system, and (ii) a natural language system. In a directed system, the system prompts the user as to what to say to the system. A natural language system is an open ended system allowing the user to say what the user wants without prompting the user about what to say.
The conventional process of building a natural language call routing system for an automatic contact center solution requires a tremendous amount of effort in the data collection process. A typical approach is to use a Wizard of OZ (WOZ) system for data collection. A WOZ system is a partially implemented system where the user hears an opening prompt and then says their request. However, instead of the system making the routing decision, a human, referred to as a “wizard,” manually routes the call. During this process, the interaction audio is saved and transcribed for the purpose of training a natural language understanding model. A WOZ system is used to collect data, from actual users, regarding how people speak. Typically, 20,000-30,000 utterances need to be collected to build the database. A WOZ system builds a database based on a statistical approach and needs data to support conclusions.
However, a WOZ system has several drawbacks. Building a WOZ system requires a significant investment in terms of capital costs. A WOZ system also requires a huge amount of effort to build. Furthermore, a WOZ system, typically, is tied to the deployment infrastructure of the particular implementation. Examples of such infrastructures are network setup, computer telephony interface (CTI) and telephony environment. CTI is a system interface that sits between the computer and the telephone in the call center. Each particular infrastructure requires customization of the WOZ system. Therefore, building a WOZ system that is a reusable asset is very difficult.
Furthermore, building a WOZ system requires agent involvement. Agents can make mistakes during data collection and their personal influence can also affect both the quality and consistency of the collected data. Even worse, these errors made by the agents will affect user experience, creating user dissatisfaction, on the system, because the data collection process is operated on a live system with real callers. Additionally, agent training is necessary and critical to the success of a WOZ system. This training is expensive and time consuming. Data clean up is also necessary in a WOZ system. All these will lead to additional cost and resources.
Therefore, it would be beneficial to have an improved method for building a natural language call routing application.